rm(list = ls())
#install.packages("plotly")
library(haven)
library(tidyverse)
library(ggplot2)
library(readxl)
library(plotly)
Importation des
données
path <- here::here()
InGrDis <- read.csv(paste0(path, "/annual-growth-in-gni-per-capita.csv"))
GenIngInd <- read.csv(paste0(path, "/gender-inequality-index.csv"))
PopGrAn <- read.csv(paste0(path, "/population-growth-annual.csv"))
Graphique I
#Filter pour le Niger
InGrDis_niger <- InGrDis %>% dplyr::filter(Region.Alpha.3.Code=="NER")
InGrDis_niger <- InGrDis_niger %>% select("Region.Name", "Start.Year", "Value")
#Afrique de l'ouest
##### Aggrégation #####
#Pour l'afrique de l'ouest
western_africa_1 <- c("Benin", "Burkina Faso", "Cabo Verde", "Côte d’Ivoire", "Gambia", "Ghana", "Guinea", "Guinea-Bissau", "Liberia","Mali","Mauritania", "Niger", "Nigeria", "Senegal", "Togo","Sierra Leone")
#Créer une variable pour recenser les pays d'Afrique de l'Ouest
InGrDis$western_africa <- ifelse(InGrDis$Region.Name %in% western_africa_1, "western Africa","world")
InGrDis_ouest <- InGrDis %>%
filter(western_africa == "western Africa")
InGrDis_ouest <- InGrDis_ouest %>%
group_by(Start.Year) %>%
summarise(Value = mean(Value))
InGrDis_ouest$Region.Name <- "western Africa"
InGrDis_ouest <- InGrDis_ouest %>%
select(Region.Name, Start.Year, Value)
###### Pour le monde #####
InGrDis_world <- InGrDis %>%
group_by(Start.Year) %>%
summarise(Value = mean(Value))
InGrDis_world$Region.Name <- "world"
InGrDis_world <- InGrDis_world %>%
select(Region.Name, Start.Year, Value)
##### Merger en superposant ######
InGrDisFinal <- rbind(rbind(InGrDis_world, InGrDis_ouest), InGrDis_niger)
###### Graphe #######
ggplot(InGrDisFinal) +
aes(x = Start.Year, y = Value, colour = Region.Name) +
# ajouter Les points
geom_point(shape = "circle", size = 2) +
#Ajouter la courbe reliant les points
geom_path(size = 0.5, show.legend = NULL) +
scale_fill_hue(direction = 1) +
#Couleurs des différentes courbes
scale_color_manual(
values = c(Niger = "#85C1E9",
`western Africa` = "#5B5B5C",
world = "#B2BABB")
)+
# Titre, titre des axes, sources...
labs(title = "Annual population growth (%)",
caption = "Source : World Bank", x=NULL, y=NULL, color = NULL) +
# Echelles des axes en abscisses
scale_x_continuous(breaks=seq(from=1960, to = 2020, by=10))+
# Echelles des axes en ordonnées
scale_y_continuous(breaks = seq(from=-7, to = 10, by=1.5), expand = c(0,0))+ #l'echelle des axes
ylim(-7,10)+
# Thèmes du fond
theme_minimal()+
theme(
# Paramètre du titre, couleur, police, position
plot.title = element_text(colour = "#0F4761", face = "italic", size=9, vjust = 8),
# Paramètre de la legende, taille...
legend.text = element_text(size = 8),
# Position de la legende
legend.position = c(0.132, 1.03),
legend.direction = "horizontal",
# Paramètres pour la source, position...
plot.caption = element_text(hjust = 0),
# Effacer les petites lignes du cadran
panel.grid.minor = element_blank(),
# Mettre les lignes horizontales en forme de tirets
panel.grid.major.y = element_line(linetype = "dashed"),
# Marge pour le cadran
plot.margin = margin(30,30,30,30))
Graphique II
#Filter pour le Niger
PopGrAn_Niger <- PopGrAn %>% dplyr::filter(Region.Alpha.3.Code=="NER")
PopGrAn_Niger <- PopGrAn_Niger %>%
select(Region.Name, Start.Year, Value)
#Afrique de l'ouest
##### Aggrégation #####
#Créer une variable pour recenser les pays d'Afrique de l'Ouest
PopGrAn$western_africa <- ifelse(PopGrAn$Region.Name %in% western_africa_1, "western Africa","world")
PopGrAn_ouest <- PopGrAn %>%
filter(western_africa == "western Africa")
PopGrAn_ouest <- PopGrAn_ouest %>%
group_by(Start.Year) %>%
summarise(Value = mean(Value))
PopGrAn_ouest$Region.Name <- "western Africa"
PopGrAn_ouest <- PopGrAn_ouest %>%
select(Region.Name, Start.Year, Value)
###### Pour le monde #####
PopGrAn_world <- PopGrAn %>%
group_by(Start.Year) %>%
summarise(Value = mean(Value))
PopGrAn_world$Region.Name <- "world"
PopGrAn_world <- PopGrAn_world %>%
select(Region.Name, Start.Year, Value)
##### Merger en superposant ######
PopGrAnFinal <- rbind(rbind(PopGrAn_world, PopGrAn_ouest), PopGrAn_Niger)
###### Graphe #######
ggplot(PopGrAnFinal) +
aes(x = Start.Year, y = Value, colour = Region.Name)+
geom_point(shape = "circle", size = 2) +
scale_color_hue(direction = 1) +
geom_line(size = 0.5, show.legend = NULL) +
scale_color_manual(
values = c(Niger = "#85C1E9",
`western Africa` = "#5B5B5C",
world = "#B2BABB")
)+
labs(title = "Annual population growth (%)",
caption = "Source : World Bank", x=NULL, y=NULL, color = NULL) +
geom_abline(intercept = 0, slope = 0, color="white")+
scale_x_continuous(breaks=seq(from=1960, to = 2020, by=10))+
scale_y_continuous(breaks = seq(0, 4, by = 1),position = "left", labels = c("", 1, 2, 3, 4), expand = c(0,0))+
coord_cartesian(ylim = c(0, 4))+
theme_minimal()+
theme(
plot.title = element_text(colour = "#0F4761", face = "italic", size=9, vjust = 8),
legend.text = element_text(size = 8),
legend.position = c(0.132, 1.03),
legend.direction = "horizontal",
plot.caption = element_text(hjust = 0),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(linetype = "dashed"),
plot.margin = margin(30,30,30,30))
Graphique III
#Filter pour le Niger
GenIngInd_niger <- GenIngInd %>% dplyr::filter(Region.Alpha.3.Code=="NER")
GenIngInd_niger <- GenIngInd_niger %>% select("Region.Name", "Start.Year", "Value")
#Afrique de l'ouest
##### Aggrégation #####
#Pour l'afrique de l'ouest
#Créer une variable pour recenser les pays d'Afrique de l'Ouest
GenIngInd$western_africa <- ifelse(GenIngInd$Region.Name %in% western_africa_1, "western Africa","world")
GenIngInd_ouest <- GenIngInd %>%
filter(western_africa == "western Africa")
GenIngInd_ouest <- GenIngInd_ouest %>%
group_by(Start.Year) %>%
summarise(Value = mean(Value))
GenIngInd_ouest$Region.Name <- "western Africa"
GenIngInd_ouest <- GenIngInd_ouest %>%
select(Region.Name, Start.Year, Value)
###### Pour le monde #####
GenIngInd_world <- GenIngInd %>%
group_by(Start.Year) %>%
summarise(Value = mean(Value))
GenIngInd_world$Region.Name <- "world"
GenIngInd_world <- GenIngInd_world %>%
select(Region.Name, Start.Year, Value)
##### Merger en superposant ######
GenIngIndFinal <- rbind(rbind(GenIngInd_world, GenIngInd_ouest), GenIngInd_niger)
###### Graphe #######
p <-ggplot(GenIngIndFinal) +
aes(x = Start.Year, y = Value, fill=Region.Name, colour = Region.Name) +
geom_point(aes(text = paste(Region.Name, ": ",
round(Value,1))),shape = "bullet", size=2) +
geom_line(size=1) +
scale_fill_hue(direction = 1) +
scale_color_manual(
values = c(Niger = "#85C1E9",
`western Africa` = "#5B5B5C",
world = "#B2BABB")
)+
scale_fill_manual(
values = c(Niger = "#85C1E9",
`western Africa` = "#5B5B5C",
world = "#B2BABB")
)+
labs(title = "Figure : Gender inequality index",caption = "Source : World Bank", x=NULL, y=NULL, color =NULL, fill=NULL)+
geom_abline(intercept = 0, slope = 0, color="white")+
scale_x_continuous(breaks=seq(from=1990, to = 2020, by=5))+
scale_y_continuous(breaks = seq(0, 0.8, by =0.2),
labels=c("",seq(0.2,0.8, by=0.2)))+
ylim(0,0.8)+
theme_minimal()+
theme(
plot.title = element_text(face = "bold", size = 10,"italic", colour = "#85C1E9"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(linetype = "dashed")
)
ggplotly(p, tooltip = "text")%>%
plotly::layout(legend=list(x=0, #Paramètre pour la légende
y = 1.04,
orientation='h'), annotations =
#Paramètre pour la source
list(x = 0.2, y = -0.08, text = "Source: UNDP",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=12, color="black")))